U.S. patent number 11,455,666 [Application Number 17/062,686] was granted by the patent office on 2022-09-27 for method and system for unusual usage reporting.
This patent grant is currently assigned to OPower, INC.. The grantee listed for this patent is OPower, Inc.. Invention is credited to Eric Chang, Richard Tyler Curtis, Tom Mercer, Daniel J. Yates, Janet Yu.
United States Patent |
11,455,666 |
Yu , et al. |
September 27, 2022 |
Method and system for unusual usage reporting
Abstract
Illustrative embodiments are directed to methods and computer
systems for reporting unusual or anomalous usage a commodity by
consumers. A computer system retrieves a set of usage-information
datasets corresponding to a set of consumers, each dataset
including past usage of the commodity during at least one of a
completed billing period and a current usage of the commodity
during the current billing period. The computer system establishes
a set of report-trigger conditions for the current billing period,
each of the report-trigger conditions corresponding to a consumer.
The computer system may monitor usage or spending of the set of
consumers to determine, for each consumer, whether an estimated
usage established for each consumer fulfills the consumer's
report-trigger condition. Once the report-trigger condition is
fulfilled, the computer system outputs a report to the
consumer.
Inventors: |
Yu; Janet (San Francisco,
CA), Mercer; Tom (San Francisco, CA), Chang; Eric
(Menlo Park, CA), Curtis; Richard Tyler (Washington, DC),
Yates; Daniel J. (Washington, DC) |
Applicant: |
Name |
City |
State |
Country |
Type |
OPower, Inc. |
Redwood Shores |
CA |
US |
|
|
Assignee: |
OPower, INC. (Redwood Shores,
CA)
|
Family
ID: |
1000006584503 |
Appl.
No.: |
17/062,686 |
Filed: |
October 5, 2020 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20210073875 A1 |
Mar 11, 2021 |
|
Related U.S. Patent Documents
|
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
13765045 |
Feb 12, 2013 |
10796346 |
|
|
|
61722334 |
Nov 5, 2012 |
|
|
|
|
61665189 |
Jun 27, 2012 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/02 (20130101); G06Q 30/0283 (20130101) |
Current International
Class: |
G06Q
30/02 (20120101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Berges Gonzalez, M. E. (2010). A framework for enabling
energy-aware facilities through minimally-intrusive approaches
(3438459). Available from ProQuest Dissertations and Theses
Professional. (845717143). Retrieved from
https://dialog.proquest.com/professional/docview/845717143?accountid=1314-
44 (Year: 2010). cited by examiner .
The brattle group; net benefits of smart meters could range from
$96 to $287 million over 20 years for utilities with a million
customers according to study led by the brattle group. (Mar. 1,
2011). Technology & Business Journal Retrieved from
https://dialog.proquest.com/ (Year: 2011). cited by applicant .
Calico energy services; calico energy solution to enable Chicago
suburb to realize a city-wide, energy-efficient smart grid. (Feb.
11, 2011). Energy Weekly News Retrieved from
https://dialog.proquest.com/professional/docview/848719225?accoutid=13144-
4 (Year: 2011). cited by applicant.
|
Primary Examiner: Harrington; Michael P
Attorney, Agent or Firm: Kraguljac Law Group, LLC
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This disclosure is a continuation of U.S. Non-Provisional
application Ser. No. 13/765,045 filed Feb. 12, 2013, entitled
"Method and System for Unusual Usage Reporting", issued as U.S.
Pat. No. 10,796,346 on Oct. 6, 2020; which claims the benefit of
U.S. Provisional Patent Application Ser. No. 61/665,189 filed Jun.
27, 2012 and U.S. Provisional Application Ser. No. 61/722,334 filed
Nov. 5, 2012, and assigned to the present assignee, which are all
incorporated by reference herein in their entirety.
Claims
What is claimed is:
1. A computer-implemented method comprising: retrieving, using one
or more processors, a plurality of usage-information datasets, each
usage-information dataset corresponding to usage of the commodity
by a consumer including past usage of the commodity; generating,
using the one or more processors, a distribution of predicted alert
occurrences across a plurality of potential threshold levels using
a set of historical customer usage data for the commodity from the
plurality of usage-information datasets; analyzing, using the one
or more processors, the distribution of predicted alert occurrences
to select a first threshold level from amongst the plurality of
potential threshold levels based at least in part on a
determination that the first threshold level is predicted to, upon
implementation as a report-trigger condition for a set of
consumers, provide for at least one alert being sent to a maximum
number of consumers of the set of consumers; establishing, using
the one or more processors, a plurality of report-trigger
conditions, each report-trigger condition corresponding to a
consumer and based at least in part on a specified percentage of a
baseline of the past usage, the specified percentage corresponding
to the first threshold level; and in response to determining, using
the one or more processors, that an estimated usage established for
a first consumer fulfills a first report-trigger condition, of the
plurality of report-trigger conditions: generating an electronic
message including information indicating that an unusual usage of
the commodity has been detected; and controlling transmission of
the electronic message, via a network connection, to an electronic
device associated with the first consumer to provide the
information indicating the unusual usage of the commodity.
2. The computer-implemented method of claim 1, wherein the
selecting the first threshold level is based at least in part on a
determination that the first threshold level is predicted to be
associated with less than a threshold number of alerts to be sent
on average to each consumer.
3. The computer-implemented method of claim 1, wherein the
selecting the first threshold level is based at least in part on a
determination that the first threshold level is predicted to be
associated with more than a threshold number of consumers receiving
at least one alert.
4. The computer-implemented method of claim 1, wherein the past
usage is defined as during a period in a season that corresponds to
a season of a current billing period.
5. The computer-implemented method of claim 4, wherein the
plurality of report-trigger conditions comprise, for each consumer,
the estimated usage for the remaining portion of the current
billing period exceeding 130 percent of the past usage during a
corresponding period.
6. The computer-implemented method of claim 1, wherein the
electronic device includes a meter or a cell phone.
7. The computer-implemented method of claim 1, wherein the
electronic message includes at least one of or a combination of
messages: an electronic mail message, a short message service (SMS)
message, an automated voice message, or an electronic signal.
8. The computer-implemented method of claim 1, wherein the first
report-trigger condition is based at least in part on at least one
of: usage of the commodity by the first consumer, or a present cost
of the commodity.
9. The computer-implemented method of claim 1, wherein determining
whether the estimated usage fulfills the first report-trigger
condition is performed after half of a current billing period has
transpired, wherein the current billing period is at least four
weeks in duration.
10. The computer-implemented method of claim 1, wherein determining
whether the estimated usage fulfills the first report-trigger
condition is performed after a pre-defined time within a current
billing period.
11. The computer-implemented method of claim 1, wherein the unusual
usage is during a current billing period, wherein the current
billing period has a duration based at least in part on at least
one of: an average of durations between three billing periods and
twelve completed billing periods, or a default period.
12. The computer-implemented method of claim 1, wherein the
threshold number is three or more.
13. The computer-implemented method of claim 1, wherein the
plurality of report-trigger conditions includes, for each consumer,
a data-value representing the consumer consenting to receiving the
electronic message.
14. The computer-implemented method of claim 1, wherein the
electronic message displays the unusual usage of the commodity as
indicia of at least one of high cost, high usage, high
environmental impact, or high carbon footprint.
15. A non-transitory machine-readable medium comprising program
code stored thereon for execution by one or more processors, the
program code configured to, when executed, cause the one or more
processors to: retrieve a plurality of usage-information datasets,
each usage-information dataset corresponding to usage of the
commodity by a consumer including past usage of the commodity;
generate a distribution of predicted alert occurrences across a
plurality of potential threshold levels using a set of historical
customer usage data for the commodity from the plurality of
usage-information datasets; analyze the distribution of predicted
alert occurrences to select a first threshold level from amongst
the plurality of potential threshold levels based at least in part
on a determination that the first threshold level is predicted to,
upon implementation as a report-trigger condition for a set of
consumers, provide for at least one alert being sent to a maximum
number of consumers of the set of consumers; establish a plurality
of report-trigger conditions, each report-trigger condition
corresponding to a consumer and based at least in part on a
specified percentage of a baseline of the past usage, the specified
percentage corresponding to the first threshold level; and in
response to determining that an estimated usage established for a
first consumer fulfills a first report-trigger condition, of the
plurality of report-trigger conditions: generate an electronic
message including information indicating that an unusual usage of
the commodity has been detected; and control transmission of the
electronic message, via a network connection, to an electronic
device associated with the first consumer to provide the
information indicating the unusual usage of the commodity.
16. The non-transitory machine-readable medium of claim 15, wherein
each of the report-trigger conditions is based at least in part on
a forecast usage of the commodity by a consumer exceeding a
pre-defined cost threshold.
17. The non-transitory machine-readable medium of claim 15, wherein
determining whether the estimated usage fulfills the first
report-trigger condition is performed after half of a current
billing period has transpired, wherein the current billing period
is at least four weeks in duration.
18. The non-transitory machine-readable medium of claim 15, wherein
determining whether the estimated usage fulfills the first
report-trigger condition is performed after a pre-defined time
within a current billing period.
19. A system comprising: a memory configured to store data; one or
more processors coupled to the memory; a communication port coupled
to the one or more processors; and a control program configured to:
control, using the one or more processors, the memory and
communication port; retrieve, using the one or more processors, a
usage-information dataset for a consumer including at least usage
data for a commodity; generate, using the one or more processors, a
distribution of predicted alert occurrences across a plurality of
potential threshold levels using the usage-information dataset,
each potential threshold level associated with a predicted number
of alert occurrences; analyze, using the one or more processors,
the distribution of predicted alert occurrences to select a first
threshold level from amongst the plurality of potential threshold
levels based at least in part on a determination that the first
threshold level is predicted to, upon implementation as a
report-trigger condition for a set of consumers, provide for at
least one alert being sent to a first number of consumers of the
set of consumers; establish, using the one or more processors, a
report-trigger condition for the consumer, the report-trigger
condition based at least in part on a specified percentage of a
baseline defined from past usage of the commodity in the
usage-information dataset, the specified percentage corresponding
to the first threshold level; and determine, using the one or more
processors, that an estimated usage for the consumer fulfills the
report-trigger condition and in response thereto: generate and
provide for display to an electronic device associated with the
consumer, an electronic message including information to reduce
usage of the commodity.
20. The system of claim 19, wherein the communication port is
configured to interface, via at least one network connection, at
least in part to at least one of a meter, a thermostat, a network,
a data exchange interface of a cellular network or a data exchange
interface to an email server.
Description
TECHNICAL FIELD
The present disclosure is related to commodity-use reporting, more
particularly to detecting and reporting of consumer's commodity
usage.
SUMMARY
In accordance with illustrative embodiments, a computer-implemented
method is provided for reducing a usage or cost of a commodity by
reporting to consumers of their unusual usage of the commodity
during a current billing period. The computer system retrieves a
set of usage-information datasets corresponding to a set of
consumers. Each dataset may include a) past usage of the commodity
during at least one of a completed billing period that may
correspond to a period of similar seasonality as the current
billing period and b) a current usage of the commodity during a
completed portion of the current billing period. The commodity
refers to a utility-based commodity, such as electricity, water,
gas, or an aggregation of several resources, such as total
energy.
In one embodiment, the computer system establishes a set of
report-trigger conditions for a set of consumers for the current
billing period, each of the report-trigger conditions corresponding
to a consumer. In the various embodiments, the computer system may
establish the report-trigger condition once for each billing
period, such as at the start of the current billing period or
before. The report-trigger condition may include at least one
unusual usage condition and at least one permissive condition. An
unusual usage condition may be defined as an estimated usage for
the current bill period exceeding a specified percentage of a
baseline defined based on the past usage. This specified percentage
may be between 100 percent and 150 percent. An unusual usage
condition may also be defined as an estimated usage for a remaining
portion of current billing period exceeding a usage threshold
defined by the past usage scaled by the specified percentage.
Unusual usage conditions may further be defined in terms of an
expected cost or an environmental impact. A permissive condition
may include a) the consumer's consent to receive the report, b) the
report being the first to be sent within the billing period, and c)
the unusual condition occurring in an actionable window during the
billing period. This actionable window maybe during portions of the
billing period that the user may act to curb their usage. For
example, in a four-week billing period, the actionable window may
include the second and third week of that period.
The computer system may continuously or intermittently monitor
usage or spending of the set of consumers to determine, for each
consumer, whether their estimated usage, established for each
consumer, fulfills their respective consumer's report-trigger
condition. The computer system may determine the estimated usage
based on a) usage for the completed portion of the current billing
period and b) a forecast of the usage for the remaining portion of
the current billing period. The forecast may be based upon a moving
or trailing history window that may vary between a five-day and
ten-day period.
Upon the estimated usage fulfilling the report-trigger condition
for a respective consumer, the computer system outputs a report to
the consumer. The report may include generating an electronic
message or outputting a signal to an intermediary service that
sends the report to the consumer. The computer system may transmit,
or cause the transmission of, the report to an electronic device
(e.g., cell phone, computer, laptop, etc.) associated with a
receiving consumer as an electronic message, such as short message
services (SMS), automated voice-messaging service, and electronic
mail ("e-mail"). Alternatively, the computer system may transmit,
or cause the transmission of, electronic signals to electronic
devices located at a consumer's premises to be displayed or relayed
and then displayed to the consumer. These electronic devices may
include thermostats or a utility meters that communicate with a
device operating as a user portal at the premises. The report may
include the unusual usage of the commodity such as unusual high
cost, unexpected costs, high usage, or higher than expected
environmental impact and/or carbon footprint, which may be measured
based on previous usage or costs of the consumer and/or comparisons
to similar consumers.
In accordance with other embodiments, the computer-implemented
method is configured to detect an unusual usage by the consumer of
the commodity, where the measured usage takes into account cost.
The method may include retrieving a set of usage-information
datasets where each of the dataset may correspond to the usage of
the commodity by a consumer. The computer system may retrieve a
cost-rate dataset for the current billing period, which may be
price-tiers or time-of-use pricing. The computer system may
establish a set of report-trigger conditions for a set of consumers
for the current billing period where each of the report-trigger
condition corresponds to a respective consumer. The report-trigger
condition may be derived from the past usage data and the pricing
data. The report-trigger condition may include a usage-trigger
condition and a cost-trigger condition. The computer system may
determine, for each consumer, whether an estimated cost that has
been established for the respective consumer for that current
billing period fulfills the cost-trigger condition, e.g., the
estimated cost exceeds an unusual cost setpoint. The computer
system may also determine whether an estimated usage established
for the same consumer has also been exceeded (i.e., a usage-trigger
condition). The computer system may output or cause to output a
report to the consumer if both the usage-trigger and the
cost-trigger conditions are fulfilled.
The described method may be employed as a computer program product,
which is stored on a machine-readable medium, comprising executable
program code to be executed, particularly, by a computer.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing features of embodiments will be more readily
understood by references to the following detailed description,
taken with reference to the accompanying drawings, in which:
FIG. 1 is a flowchart illustrating a computer-implemented method of
reducing a usage or cost of a commodity by reporting to a consumer
of an unusual usage of the commodity according to the illustrative
embodiment.
FIG. 2A shows a plot of a daily usage of electricity far a given
consumer over a given year,
FIG. 2B shows the usage of FIG. 2A cumulated for each billing
cycle,
FIG. 3 is a diagram of report-trigger conditions according to the
illustrative embodiment.
FIG. 4 is a diagram of report-trigger conditions according to an
alternate embodiment.
FIG. 5 illustrates aspects of forecasting of the consumer's usage
for the remaining portion of the current billing period.
FIG. 6 is a diagram illustrating an operational aspect of the
embodiments to determine, for each consumer, whether an estimated
usage that has been established for the respective consumer for
that current billing period fulfills the report-trigger
condition.
FIG. 7 is a flowchart of a computer-implemented method of reducing
a usage or cost of a commodity by reporting to a consumer of
unusual cost according to an alternate embodiment.
FIG. 8 is a diagram of a system according to the illustrative
embodiment.
FIG. 9 shows a flowchart of a computer-implemented method according
to an alternate embodiment.
FIG. 10 shows embodiment according to the described illustrative
embodiment.
FIG. 11 illustrates a plot of a distribution of customers having
usages exceeding their historical averages.
FIG. 12 illustrates an electronic mail report according to an
illustrative embodiment.
DETAILED DESCRIPTION
Providing an alert of unusual or anomalous utility usage allows
consumers to promote conservation by reducing resource consumption,
and to manage theft finances. Consumers may set thresholds of when
an alert or report would be sent to them. However, the thresholds
by themselves are insufficient in curbing consumers' usage and
spending behavior. For example, the threshold may trigger an alert
or report that may be late. When a significant portion of the
billing period remains, the consumers may need to continue to spend
or use the commodity. Consumers may set a lower threshold in order
to receive an earlier warning. However, in order to do so, the
consumers would have to understand their usage patterns to set the
proper setpoint. Further compounding this problem is the
variability in the consumers' usage patterns and the effects from
seasonality. Importantly, reducing resource consumption (e.g.,
usage of electricity) causes less power to be drawn from an
electricity grid, which causes less power to be generated by an
electrical power system. This improves the efficiency of the power
system. It is understood that when too much power is consumed and
drawn from the electrical power system, a power outage may occur in
the electrical power system. Power outages, black outs and brown
outs are technical problems and very undesirable. Reducing resource
consumption by targeting users with usage reports causes energy
reduction and helps to avoid a power outage. This improves the
power system.
Illustrative embodiments are directed to computer-implemented
methods and systems for reporting unusual or anomalous usage of a
commodity by consumers. In various illustrative embodiments, a
report is provided to the consumers or to a utility or report
service provider to relay to the consumers. The report may be
electronic or may be generated as a physical hard copy that is
mailed to the consumers. Illustrative embodiments further define
usage patterns that constitute as being unusual while accounting
for seasonality and allowing for the earlier detection of certain
usage to curb the consumers' usage or spending.
As used herein, the term "report" generally refers to a signal that
results in a visual or an audible indication. Report also refers to
the transmission of a signal that results in the giving of such an
indication.
The term "usage" refers to a quantity of use, a cost associated
with the use, or a quantified metric representing the use or cost,
such as environmental impact.
The term "commodity" refers to a utility-based commodity, such as
electricity, water, and natural gas, which are consumable finite
resources delivered to a fixed structure. Commodity also refers an
aggregation of such resources, such as total energy.
FIG. 1 is a schematic flowchart illustrating a computer-implemented
method 100 of reducing a usage or cost of a commodity by reporting
to a consumer of an unusual usage of the commodity according to the
illustrative embodiment. The operation is performed during and for
a current billing period. Typically, a utility company bills a
consumer at the end of a usage period. As such, the current billing
period refers to the time in which the consumer is consuming the
commodity to be billed.
The method includes a computer system retrieving 102 a set of
usage-information datasets, each corresponding to the usage of the
commodity by a set of consumers. For example, the usage-information
dataset may include a) past usage of the commodity for a completed
billing period and b) a current usage of the commodity during a
completed portion of the current billing period.
The computer system may retrieve past usage data from a consumer's
old utility bills. These old utility bills may be referred herein
as completed billing periods. These bills may be stored in a
database operated by the utility, by an archival provider, or by a
service provider that sends the unusual usage report to the
consumer. Past usage data may also be stored-meter readings that
correspond to consumption of the commodity by the consumer.
The current usage of the commodity refers to the most recent, i.e.,
the last reading (or the last series of readings), from a usage
monitoring device (i.e., a utility meter). Current usage may be
referred in temporal terms, such as being within the last hour or
day. The meter reading may be performed by a utility company or an
independent service provider that provides the meter reading to the
utility.
The computer system may perform this monitoring operation at a
defined schedule during the billing period, such as at every hour,
fractions of the hour or day, daily, or weekly. Alternatively, the
computer system may perform this monitoring operation at specified
events, such as when new usage data is retrieved from the meter or
made available from a utility database. The computer system may
also perform the monitor operation at a specified portion of the
current billing period, such as during the middle of the billing
period.
In the various embodiments, the computer system may retrieve
current usage data from a database. The current usage data may be a
meter reading, but may also be other usage collection device on the
premises, such thermostats, sub-meters, and energy management
systems.
Past usage for a consumer may also be derived from other consumers.
In instances where a consumer has a limited usage history, such as
at the beginning of a new service with the utility company, the
computer system may use historical usage data of other consumers
that have similar home characteristics to form the past usage for
this new consumer. These home characteristics may include
information, such as size of the premises (in square feet), number
of occupants, general age of the premises, dwelling type, presence
of a pool, meter reading cycle, distance from each other, and
geographic location. In such embodiments, the computer system may
analyze datasets of multiple consumers having several or all of
these characteristics. Alternatively, the utility company may have
estimates of past usage based on geographic location. These
estimates may also serve as past usage data for new consumers.
Generally, a premises is equipped with a meter to monitor the usage
of the commodity at the premises. A utility company providing the
commodity to the premises typically performs a read of the meter in
each billing cycle, typically on a monthly basis. FIG. 2A shows a
sample plot of a daily usage 202 of electricity for a given
consumer over a given year, FIG. 2B shows the usage of FIG. 2A
cumulated for each billing cycle 206. The current usage 208 of the
present billing period 210 may be referred to as the current usage
of the commodity during a completed portion of the current billing
period.
The computer system may establish 104 a report-trigger condition
for the current billing period at the beginning or before the
period, preferably before the beginning of the period.
Alternatively, the report-trigger condition may be established
during the current billing period, such as immediately before the
report-trigger condition is used in the monitoring for unusual
usage. This report-trigger condition is based at least in part from
the past usage in the usage-information dataset.
FIG. 3 is a diagram of report-trigger conditions according to the
illustrative embodiment. The report-trigger condition 302 may
include a set of conditions, such as triggering conditions as well
as permissive conditions. A triggering condition refers to the
unusual or anomalous condition to be monitored. More than one
triggering conditions may be in a report-trigger condition. For
example, a triggering condition may be based upon usage, cost,
and/or environmental impact. The triggering conditions may be
independently employed or in combinations. A triggering condition
304 based on usage may be defined by Eqn. 1, where the triggering
condition is the actual usage of the commodity, U' actual,
exceeding the unusual usage profile, U.sup.i.sub.unusual, at "time
i." Triggering condition=U.sub.actual.sup.i>U.sub.unusal.sup.i
(Eqn. 1)
The actual usage, U.sup.i actual, of the commodity generally refers
to a meter reading or a usage reading. The variable "time i" refers
to instances of time during the current billing period. It may be
expressed in synchronous intervals, such as by minutes, tenth of an
hour, quarter of an hour, hours, or days. For example, "time i" may
be in 15-minute increments. Alternatively, the variable "time i"
may be asynchronously acquired. R may, for example, be expressed in
terms of event identifiers, such as when the meter or usage is
read.
The unusual usage profile is the triggering condition that defines
an unusual usage. It may be defined by Eqn. 2, where the profile is
a baseline historical usage, U.sub.historical, scaled by a
threshold percentage, T.sub.percentage. The historical usage,
U.sub.historical serves as a baseline. The profile may be linear
over the number of days in the billing period, N.
.times..times. ##EQU00001##
Threshold percentage is referred herein as the pre-defined
threshold. The threshold percentage is preferable established
around 130 percent. However it may vary between 100 percent and 200
percent. The consumer may receive recommendations of the setpoint
to use and how it may correspond to various conservation efforts.
FIG. 11 illustrates a plot 1100 of a distribution of customers
having usages exceeding their historical averages. In the x-axis
1102, a percent of usage exceeding the historical averages is
shown, Here, the data is analyzed between 0% and 200%. The percent
of usage exceeding the historical averages refers to the threshold
percentage. In the y-axis 1104, a percent of customer having a bill
in a billing period that exceeds the threshold percentage is shown.
As such, for a given threshold percentage, the computer system may
determine the percentage of customers who may receive an alert. The
historical average may be based on the customer's own usage history
and may be compared based on the bill period (e.g., same month),
Here, at a threshold percentage of 140%, approximately 15% of the
customers have at least one bill that exceeds this threshold.
Alternatively, the T.sub.percentage value may be established such
that fewer than three alerts are expected to be sent on average to
each consumer. The T.sub.percentage value may also be established
to maximize the number of consumers that would receive a report.
For example, the threshold may be established by using a set of
historical customer usage data for a given utility and determining
the distribution of alert occurrences for different threshold
levels. The distribution may be analyzed using a histogram. The
T.sub.percentage value may be the threshold level with the maximum
number of customers receiving at least one alert. The analysis may
be performed across several seasons to reduce seasonal
variability.
The baseline historical usage, U.sub.historical, may be derived
from past usage during similar seasonality. In the various
embodiments, the past usage may correspond to usage from one year
ago. The past usage data may be an average for the same month
across different years.
The triggering condition 306 may include unusual or anomalous cost.
Eqn. 3 defines such a triggering condition where the actual cost of
the commodity, C.sup.i.sub.actual, exceeds the unusual cost
profile, C.sup.i.sub.unusual, at "time i." Triggering
condition=C.sup.iactual>C.sup.iunusual (Eqn. 3)
The unusual cost profile, C unusual, may be defined by Eqn. 4,
where the profile is a baseline historical cost, C.sub.historical,
scaled by a threshold percentage, T.sub.percentage. The profile may
be linear over the number of days in the billing period, N.
.times..times. ##EQU00002##
Additionally, a consumer may personalize his or her threshold by
providing a preference on the frequency of the alert. As a result,
rather than an input of the threshold percentage, the system may
provide options for a desired alert frequency, such as "normal",
"more frequent", or "less frequent". Then based upon the selected
desired alert frequency, the computer system may adjust a
personalized threshold percentage T.sub.percentage_customer_x for a
given customer X. For a normal alert frequency, the computer system
may determine a T.sub.percentage value that would provide, for
example, at least three alerts in a year. As such, a "more
frequent" alert selection may correspond to a T.sub.percentage
value that would provide an additional alert (e.g., four alerts).
And a "less frequent" alert selection may correspond to a
T.sub.percentage value that would provide fewer alerts (e.g., two
alerts).
FIG. 4 is a diagram of report-trigger conditions according to an
alternate embodiment. The trigger condition for unusual cost may
include both the cost and usage baseline exceeding a threshold. For
example, the current billing period is May of Year 1. In May of
Year 0, a consumer spent $100 for 100 kWh of electricity. For a
threshold percentage of 130 percent, the triggering threshold may
be established at $130 and 130 kWh. In this embodiment, for the
triggering condition to be fulfilled, the estimated cost for the
end of May would have to exceed $130 and that 130 kWh would have
been used. Since the condition is based upon cost and usage, the
system avoids reporting due merely to price fluctuations of the
commodity. It should be appreciated by those skilled in art that
differing threshold percentage values may be applied for cost and
usage.
Alternatively, the unusual cost may be expressed in term of a
change to environmental impact. Environmental impact may include,
for example, carbon foot-print (shown in tons of carbon).
Environmental impact may also be expressed in terms of equivalents
to other environmental quantities, for example, the number of miles
driven, etc. Consumers may not understand their environmental
impact based upon the tonnage of carbon that they generated, thus,
the report may be alternatively expressed in relation to a more
tangible reference. For example, the report may say that "the
energy required to heat and/or to cool the home is equivalent to
the consumer driving X number of miles." The report may also be
expressed in a manner to promote conservation. For example, where
the consumer has exceeded his or her usage, the report may suggest
that the consumer should drive a certain number of miles in order
to offset the environmental impact of the extra energy used to heat
and/or cool the home.
The environmental benefits or impacts may be calculated using data
stored in a look-up-table and applied to the cost and/or usage
information. The computer system may use environmental benefits
information as published by various government entities or other
similar databases.
Referring back to FIG. 3, the report-trigger condition 302 may
include permissive conditions, such as whether a consumer has given
consent to receive the report 308 and whether the report will be
the first report sent to the consumer during the current billing
period 310. Permissive conditions may be referred to as eligibility
conditions. Permissive conditions may promote the consumer's
experience of receiving the reports. For example, in instances
where the consumer did not provide consent to receive the report;
the receipt of the report may cause confusion. Similarly, overly
alerting the consumer may also result in the consumer Ignoring the
report. Other permissive condition may include the consumer being a
residential consumer.
Another permissive condition may include whether the unusual usage
is detected in the middle of the billing period 312. For example,
in a four-week billing period, a two-week monitoring window may be
employed as a permissive or eligibility condition.
Referring back to FIG. 1, the method may determine 106 an estimated
usage for the current billing period for the remaining portion of
the current billing period and determine 108 whether the estimated
usage fulfills the report-trigger condition, Upon the
report-trigger condition being fulfilled, the method includes
outputting 110 a report.
FIG. 5 illustrates aspect of forecasting the consumer's usage for
the remaining portion of the current billing period. In various
embodiments, the forecast of the consumer's usage for the remaining
portion 502 of the current billing period may be based upon a
trailing history 504 in the completed portion of the current
billing period. The beginning 506 of the current billing period
starts on Day 1 of the billing period. In this scenario, the
current day 512 is Day 15. The remaining portion 502 of the current
billing period is the remaining 16 days (Days 16-31) in a billing
period of 31 days. When the trailing history 504 is longer than the
completed portion of the current billing period, data from the
previous completed billing period may be used. For example, at Day
2 of the May, the trailing history window may include Day 1 of May
as well as the last six days of April.
In the various embodiments, the trailing history data 508 may be
duplicated to serve as the forecast 510 for the remaining portion
of the current billing period. Where the remaining portion 502 of
the current billing period is longer than the trailing history 504,
the trailing history data 508 may be duplicated multiple times to
generate forecast data 516 and an end-of-the-forecast data 518. The
end-of-the-forecast data 518 may be shorter than the forecast data
516 depending on the length of the remaining portion 502 of the
current billing period. The forecast data 516 may be duplicated as
a mirror of (i.e., reversed from) the trailing history data 508.
This scheme provides more weight to the most recent usage data
among the trailing history data 508, because the
end-of-the-forecast data 518 is based on the most recent data from
the trailing history data 508, Alternatively, the forecast data 516
may merely be duplicated in the same sequence as the trailing
history data 508. Where the report explains the forecasting method
to the consumer, this approach may be more easily understood by
consumers.
In an embodiment, the trailing window data 508 may also be averaged
to provide an average rate of usage. The average rate of usage may
then be used to extrapolate the forecast usage for the remaining
portion 502 of the current billing period.
For one embodiment, the inventors discovered that a seven-day to
ten-day trailing window provides a reasonable usage estimation of
the consumer's usage trend of up to a month. This window size
beneficially accounts for the high and low usage during a time
period, while also remaining in the same weather period. This
window is also sufficiently small to not be affected by seasonality
effects, which may skew the forecast.
It should be appreciated by those skilled in the art that longer or
shorter trailing history may be employed without imparting from the
disclosed embodiments. For example, by modeling weather patterns,
the computer system may account for weather effects beyond the
seven-to-ten days trailing window.
The computer system may determine 106 whether the estimated usage
fulfills the report-trigger condition at pre-defined schedules
during the current billing period. Where communicating meters are
deployed, the meter may be read at one-minute, 15-minute, hourly,
or daily increments. The operation may be performed to correspond
to a new meter reading.
Referring back to FIG. 1, the computer system determines 108
whether the estimated usage fulfills the report-trigger condition.
FIG. 6 is a diagram illustrating an operational aspect of the
embodiments to determine, for each consumer, whether an estimated
usage that has been established for the respective consumer for
that current billing period fulfills the report-trigger condition.
In the various embodiments, the past usage data 602 is scaled by
the T.sub.percentage cen value (for example, 130%) to establish a
triggering threshold 604. In this example, at current day 512 (Day
15), the forecast usage 606 exceeds the triggering threshold 604 at
Day 18, thus fulfilling the report-trigger condition.
Alternatively, the triggering condition may be based on the
forecast usage 606 exceeding the triggering threshold 608
established at the end of the billing period. As shown, an unusual
usage pattern by a consumer is detected within a few days of the
elevated usage beginning. As such, the consumer has time to take
action to curb their usage or spending.
A component of the estimated usage may include its duration, which
establishes the length of the billing period. In the various
embodiments, the current billing period may have a duration based
on an average of three billing periods. Other forecast lengths may
be employed, such as a fixed length.
In operation 110, the computer system outputs a report upon the
report-trigger condition being fulfilled, indicating an unusual
usage or unusual usage pattern by the consumer is detected. The
report may be formed in different ways. For example, it may include
generating an electronic message, which may be an electronic mail
message, a short message service (SMS) message, and/or an automated
voice message. The report may be sent directly to consumers via
such electronic messages. The report may also be sent to
intermediaries, such as the utility companies or a service
provider. The report may also be signals send to an electronic
device located at a location associated with the consumer, such as
a meter or a thermostat. The meter may be an advanced meter
infrastructure (AMI) based meter or a meter with communication
capabilities over a network.
The message may be personalized. The personalization provides
greater confidence to the consumer that the information is
relevant. For example, the personalization may include the alert
type (unusual usage or cost), fuel type, as well as tips and
education material to reduce usage. As a result, the likelihood of
the consumer taking action increases such as to reduce usage.
The message may be formatted for electronic mail as shown in Table
1.
TABLE-US-00001 TABLE 1 Email subject: Unusual <electric/natural
gas> usage Header info: Text here is the same for all cases,
unless noted Text Description Intro stmt You're receiving Short
term: this links to SmartSource this alert to help you for the user
to opt-out of all emails keep your bills low. from Opower
Unsubscribe Long term: Would be great if this could either link to
astro or a configuration page so that you don't need to opt-out of
all Opower emails (e.g. reports and unusual usage alerts) Account
Acct # ******XXXX show asterisks for most digits. Only number
showing actual last 4 digits Alert title Unusual <electric/ This
is the same as the email subject natural gas> usage Budget This
is not a bill: Only shown for customers on budget bill You're on
budget billing. Otherwise should be omitted. statement billing.
For short message service (SMS) message, the report may say
"UTILCO: based on your recent Electric use, your projected bill is
$102. Tip: Adjust the temp 3-5 F to lower your bill." The report
may also say "UTILCO: Your recent Electric use is 14% higher than
typical tar you this time of year. Tip: Adjust the temp 3-5 F to
lower your bill."
The report may include literature to improve usage reduction. The
literature may include tips as shown in Table 2.
TABLE-US-00002 TABLE 2 Electricity (summer and Natural Gas (summer
and winter) Impact winter) Impact Steps to take <same> Turn
off unused lights & 1/5 Shave a minute off 1/5 devices shower
time Clean or replace air 3/5 Clean or replace air 3/5 filters
monthly filters monthly Adjust your thermostat 5/5 Adjust your
thermostat 5/5 3-5.degree. 3-5.degree. See more ways to save
<same>
The consumer may also customize the settings for the report, such
as the thresholds (maximum or minimum), as well as the maximum
number of alerts to be received per billing period. The consumer
may also indicate preferences on the method of delivery of the
report.
FIG. 12 illustrates an electronic mail report according to an
illustrative embodiment. As shown, the report includes the
introductory statement 1202, the account number 1204, the alert
title 1206, and the literature to improve usage reduction 1208.
The described method may be configured to monitor large numbers of
consumers while distinguishing unusual usage from ordinary usage
that is subject to ordinary fluctuations. In enabling consumers to
manage their commodity use, the various embodiments provide for
incremental efficiency results as well as greater consumer
satisfaction. Additionally, the method may improve the user
experience in ensuring that as many people as possible receive
alerts and that the alert would be relevant and useful to them.
This configuration may entail balancing the frequency of reporting
with the frequency of false-positive condition when establishing
the triggering threshold.
FIG. 7 is a flowchart of a computer-implemented method of reducing
a usage or cost of a commodity by reporting to a consumer of
unusual cost according to an alternate embodiment.
A computer system may retrieve usage-information dataset
corresponding to the usage of the commodity by the consumer 702,
the usage-information dataset including past usage of the commodity
during at least one of a completed billing period and a current
usage of the commodity during a completed portion of the current
billing period. The computer system may retrieve a cost-rate
dataset for the current billing period 704. The cost-rate dataset
may include tiered rates as well as peak and off-peak pricing.
The computer system may establish a report-trigger condition for
the current billing period; the report-trigger condition being
based from the past usage in the usage-information dataset 706. The
computer system may determine whether an estimated cost for the
current billing period fulfills the report-trigger condition 708
and outputs a report to the consumer 710 upon such conditions.
FIG. 8 is a diagram of a system according to the illustrative
embodiment. The system reduces a usage or cost of a commodity by
reporting to a consumer of an unusual cost or spending directed to
use of a commodity. The system 800 includes a memory 802,
communication ports 804 a-c, and a control program 806.
The memory 802 is configured to store usage data 808 associated to
a commodity. The usage data may include, in part, a
usage-information dataset corresponding to usage of the commodity
by a plurality of consumers. The usage-information may include, in
part, past usage-information of the commodity during at least one
of completed billing period and a current usage of the commodity
during a completed portion of the current billing period. The past
usage data may include bill data 809. The communication port 804 c
may be configured to transmit report data to a set of consumers
810, while the communication port 804 a is configured to receive
data with the utility database. The control program 806 is
configured to control the memory 802 and the communication ports
804 a-c. The control program 806 may include a forecast module 814,
a rate module 816, a monitor module 818, and a report module 820.
The control program 806 may also retrieve the usage-information
dataset for a consumer, via communication port 804 b that
interfaces directly to meters 812. The control program 806 may
establish a report-trigger condition for the current billing period
for the consumer, where the report-trigger condition may be based
at least in part from the past usage in the usage-information
dataset. The control program 806 may determine an estimated usage
for the current billing period based at least, in part, on usage
for the completed portion of the current billing period and a
forecast of usage for a remaining portion of the current billing
period. The forecast of the usage may be generated by the forecast
module 814. The control program 806 may continuously determine
whether the estimated usage fulfills the report-trigger condition
using the monitor module 818. If the estimated usage fulfills the
report-trigger condition, the monitor module 818 may cause the
report module 820 to output a report to the consumers 810 through
the communication port 804 c.
The communication ports 804 b may be configured to interface to a
meter, a thermostat, a data exchange interface of a cellular
network, and other networks.
It should be appreciated by those skilled in the art that the
communication ports 804 a-c may be implemented individually or in
combination. For example, the communication ports may be combined
to a single Internet port that interface to a local area network.
The various communication ports may be implemented on several
servers that interface to the respective networks. For example, the
communication port 804 b may be a server that interfaces to utility
meter. One example is a gateway FTP server to interface with
utilities servers to avow for the transfer of data files. The
communication port 804 c may be implemented as an exchange server,
such as UUA for short message service (SMS) gateways, electronic
mail (email) using simple mail transfer protocol (SMTP), as well as
interactive voice response (IVR). Application programming interface
(API) may, for example, be used to control IVR and SMS.
FIG. 9 shows a flowchart of a computer-implemented method according
to an alternate embodiment. A computer system may establish an
average length of billing periods for the customer 902 to determine
the end of the current billing period. For example, the average
length of the bill periods may be between 28-31 days where the
current bill period is monthly. The average length of the bill
periods may be based on old bills including the past three billing
periods up to the past twelve. In instances where less than three
old bills exist, the computer system may use a default value
provided by the utility company. The average length of the billing
periods may be used to filter anomalously short billing periods,
which may occur for new customers. As discussed, a bill period may
be monthly, but other durations may be employed, including, for
example, 45-days, 60-days, and 90-days.
The computer system may retrieve a usage data for the current
billing period 904 to establish the end date of the current billing
period. The computer system may retrieve the last billing usage
read to determine the start date of the current billing period and
average the billing period to project the end date of the current
billing period.
The usage data may be in the data format as shown in Table 3
TABLE-US-00003 TABLE 3 Rates modeled text Not modeled text Recent
data header Your last X days <same> Recent data $XXX XXX
<kWh/therms/CCF> Recent data date Mmm DD-Mmm DD <same>
range Link to astro: View your usage <same>
The computer system may determine the usage for the remaining days
in the billing period 906 by determining the usage to date of the
current billing period and forecasting the remaining days in the
current billing period. The forecasting may be based on the
trailing window of the usage that may be between seven to ten
days.
The computer system may use tiered rates, or peak and off-peak
pricing to determine a projected cost for the remaining portion of
the billing period 908. The computer system may store to a database
the projected billing period, the usage and cost to date, the
forecasted usage and cost.
The computer system may compare the forecast to a baseline to
determine whether an unusual usage exists 910. The unusual usage
may be defined as a baseline value, established from old bills of
similar seasons, exceeding a defined threshold. If the condition is
fulfilled, the computer system may verify the eligibility criteria
to ensure the customer has opted in to receive the alerts, that the
customer is residential, and that the report would be sent during
the middle of the billing period 912. The unusual usage may be
based on cost or usage. If cost is the trigger, the report may be
outputted only if both the usage and cost threshold are exceeded
914. This condition would discount the effect of price increases,
rather than usage increase.
FIG. 10 shows embodiment according to the described illustrative
embodiment. The server 1002 receives data (i.e., historical and
usage information) from the utility 1006. The server 1002 performs
the computer-implemented method described above. If a
report-trigger condition is satisfied, the server 1002 may
communicate the information to consumers associated with those
buildings 1008, 1010, and 1012. In various embodiments, the server
1002 communicates the report, or the alert, via the communications
network 1014. For example, the server 1002 may send the report
and/or alert in an e-mail. Alternatively, the consumer may log into
a server supporting website 1004 and view the alert or the
personalized message corresponding to the report. In addition, the
server 1002 may print the report or may provide the information to
a printing system so that the data can be provided to the consumer
via regular mail (e.g., as part of a utility bill). In other
embodiments, the report/alert is communicated back to the utility
company 1006 so that the utility company 1006 can provide the
report/alert to the consumer 1008, 1010, and 1012.
It should be noted that terms such as "processor" and "server" may
be used herein to describe devices that may be used in certain
embodiments of the present system and should not be construed to
limit the present system to any particular device type or system
unless the context otherwise requires. Thus, a system may include,
without limitation, a client, server, computer, appliance, or other
type of device. Such devices typically include one or more network
interfaces for communicating over a communication network and a
processor (e.g., a microprocessor with memory and other peripherals
and/or application-specific hardware) configured accordingly to
perform device and/or system functions. Communication networks
generally may include public and/or private networks; may include
local-area, wide-area, metropolitan-area, storage, and/or other
types of networks; and may employ communication technologies
including, but in no way limited to, analog technologies, digital
technologies, optical technologies, wireless technologies,
networking technologies, and internetworking technologies.
The various components of the control program may be implemented
individually or in combination. For example, each component may be
implemented or a dedicated server or a set of servers configured in
a distributed manner.
It should also be noted that devices may use communication
protocols and messages (e.g., messages created, transmitted,
received, stored, and/or processed by the system), and such
messages may be conveyed by a communication network or medium.
Unless the context otherwise requires, the present system should
not be construed as being limited to any particular communication
message type, communication message format, or communication
protocol. Thus, a communication message generally may include,
without limitation, a frame, packet, datagram, user datagram, cell,
or other type of communication message. Unless the context requires
otherwise, references to specific communication protocols are
exemplary, and it should be understood that alternative embodiments
may, as appropriate, employ variations of such communication
protocols (e.g., modifications or extensions of the protocol that
may be made from time-to-time) or other protocols either known or
developed in the future.
It should also be noted that logic flows may be described herein to
demonstrate various aspects of the system or method, and should not
be construed to limit the present system or method to any
particular logic flow or logic Implementation. The described logic
may be partitioned into different logic blocks programs, modules.
Interfaces, functions, or subroutines) without changing the overall
results or otherwise departing from the true scope of the system or
method. Often times, logic elements may be added, modified,
omitted, performed in a different order, or implemented using
different logic constructs (e.g., logic gates, looping primitives,
conditional logic, and other logic constructs) without changing the
overall results or otherwise departing from the true scope of the
present disclosure.
The present system or method may be embodied in many different
forms, including, but in no way limited to, computer program logic
for use with a processor (e.g., a microprocessor, microcontroller,
digital signal processor, or general purpose computer),
programmable logic for use with a programmable logic device (e.g.,
a Field Programmable Gate Array (FPGA) or other programmable logic
device (PLD)), discrete components, integrated circuitry (e.g., an
Application Specific Integrated Circuit (ASIC)), or any other means
including any combination thereof. In a typical embodiment of the
present system or method, predominantly all of the described logic
is implemented as a set of computer program instructions that is
converted into a computer executable form, stored as such in a
computer readable medium, and executed by a microprocessor under
the control of an operating system.
Computer program logic implementing all or part of the
functionality previously described herein may be embodied in
various forms, including, but in no way limited to, a source code
form, a computer executable form, and various intermediate forms
(e.g., forms generated by an assembler, compiler, linker, or
locator). Source code may include a series of computer program
instructions implemented in any of various programming languages
(e.g., an object code, an assembly language, or a high-level
language such as Fortran, C, C++, JAVA, or HTML) for use with
various operating systems or operating environments. The source
code may define and use various data structures and communication
messages. The source code may be in a computer executable form
(e.g., via an interpreter), or the source code may be converted
(e.g., via a translator, assembler, or compiler) into a computer
executable form.
The computer program may be fixed in any form (e.g., source code
form, computer executable form, or an intermediate form) either
permanently or transitorily in a tangible storage medium, such as a
semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or
Flash-Programmable RAM), a magnetic memory device (e.g., a diskette
or fixed disk), an optical memory device (e.g., a CD-ROM), a PC
card (e.g., PCMCIA card), or other memory device. The computer
program may be fixed in any form in a signal that is transmittable
to a computer using any of various communication technologies,
including, but in no way limited to, analog technologies, digital
technologies, optical technologies, wireless technologies,
networking technologies, and internetworking technologies. The
computer program may be distributed in any form as a removable
storage medium with accompanying printed or electronic
documentation (e.g., shrink wrapped software), preloaded with a
computer system (e.g., on system ROM or fixed disk), or distributed
from a server or electronic bulletin board over the communication
system (e.g., the Internet or World Wide Web).
Hardware logic (including programmable logic for use with a
programmable logic device) implementing all or part of the
functionality previously described herein may be designed using
traditional manual methods, or may be designed, captured,
simulated, or documented electronically using various tools, such
as Computer Aided Design (CAD), a hardware description language
(e.g., VHDL or AHDL), or a PLD programming language (e.g., PALASM,
ABEL, or CUPL).
Programmable logic may be fixed either permanently or transitorily
in a tangible storage medium, such as a semiconductor memory device
(e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a
magnetic memory device (e.g., a diskette or fixed disk), an optical
memory device (e.g., a CD-ROM), or other memory device. The
programmable logic may be fixed in a signal that is transmittable
to a computer using any of various communication technologies,
including, but in no way limited to, analog technologies, digital
technologies, optical technologies, wireless technologies (e.g.,
Bluetooth), networking technologies, and internetworking
technologies. The programmable logic may be distributed as a
removable storage medium with accompanying printed or electronic
documentation (e.g., shrink wrapped software), preloaded with a
computer system (e.g., on system ROM or fixed disk), or distributed
from a server or electronic bulletin board over the communication
system (e.g., the Internet or World Wide Web). Of course, some
embodiments of the systems or method may be implemented as a
combination of both software (e.g., a computer program product) and
hardware. Still other embodiments may be implemented as entirely
hardware, or entirely stored software.
The embodiments described above are intended to be merely
exemplary; numerous variations and modifications will be apparent
to those skilled in the art. All such variations and modifications
are intended to be within the scope of the claims.
* * * * *
References